Deep Learning & Network Security
A possible fruitful combination for efficient network security?
Cyber-security & security in Wireless Sensor Networks (or WSNs) are topics considerable focus in today’s world which is switching to digital modes of transactional protocols and methods. However, their wireless nature makes them prone to immense number of attacks, which when exploited can lead to disastrous results. There have been significant advancements in dealing with attacks at professional level of knowledge in network security. This could contribution in either or both of the two ways: Detection & Elimination (of malicious entities). But, doing so has resulted in generation of 2 broad and significant issues.
Issues:
1. Accuracy: These methods often are newbies and can provide only a certain level of accuracy, which most of the times isn’t enough to deploy them at practical sites.
2. Speed/Efficiency: Methods which are expected to provide a greater accuracy in either or both of the phases viz. detection and elimination, take a longer time to execute which is usually beyond the feasible and acceptable time-frame to allow execution of subsequent actions by the administrators.
Machine Learning As A Solution:
Machine learning seems to be an opportunistic solution which can be explored to address the above issues. But, machine learning does not only limit to merely applying established algorithms to cyber-entities. According to some newly done research studies, some scholars have presented this problem to the AI and deep learning community a broader and new perspective to think upon this by providing related datasets.
While, the identification of such attacks is a very cumbersome and difficult process, deep learning techniques can prove to be a helping hand with respect to the aforementioned issues.
Limited availability to datasets:
Though, deep learning or machine learning can prove to be useful techniques, there also exists another problem — the limited availability of datasets. Datasets required for analysis by the deep learning models are not available in appropriate forms. There are several challenges which need to addressed to be able to provide the community with an abundance of such datasets including but limited to the metrics and the labels in consideration. This brings us to the next point.
Lack of skilled domain experts:
Current scenario clearly depicts that experts exist only in the exclusive fields either in AI or Network Security. Due the minimal knowledge of inter-domain skills of these two fields, the ability to create and work with the datasets, machine learning models, and network security concepts simultaneously has been hindered quite a lot. This results in lack of labelled samples, numerous labeling errors as well as unbalanced datasets.
What can be done?
There lies immense opportunity in combining the concepts of deep learning with the network security and cyber-security to generate effective solutions in problems such as detection and elimination of attacks. The possible obstacles that can hinder this process have been discussed earlier. Resolution of these obstacles can be considered as a milestone in the journey. The treasure that lies within this combination can be harnessed and brought into action which can pave way for incredible and immensely efficient algorithms which will not only give real-time security solutions and give multi-layered security to the wireless sensor networks but also reduce the errors caused during the frequent manual interference for corrective actions in the network.
Thanks for reading. Coming up with another article to give an insight on how we have contributed to minimizing the problems on the attacks on wireless sensor networks. Comments and inputs are welcomed. Follow me on LinkedIn here! Remember to 👏 if you enjoyed this article. Cheers!
